Overview

Dataset statistics

Number of variables94
Number of observations177
Missing cells31
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory148.4 KiB
Average record size in memory858.7 B

Variable types

Numeric6
Categorical88

Alerts

EstateType has constant value ""Constant
DistributionType has constant value ""Constant
Object_price is highly overall correlated with LivingSpace and 2 other fieldsHigh correlation
LivingSpace is highly overall correlated with Object_price and 2 other fieldsHigh correlation
Rooms is highly overall correlated with LivingSpace and 2 other fieldsHigh correlation
ConstructionYear is highly overall correlated with erstbezug and 3 other fieldsHigh correlation
dach_ausgebaut is highly overall correlated with dslHigh correlation
denkmalgeschuetzt is highly overall correlated with Object_price and 2 other fieldsHigh correlation
dsl is highly overall correlated with dach_ausgebaut and 1 other fieldsHigh correlation
dusche is highly overall correlated with fenster and 1 other fieldsHigh correlation
erstbezug is highly overall correlated with ConstructionYear and 3 other fieldsHigh correlation
fenster is highly overall correlated with dusche and 1 other fieldsHigh correlation
fu\u00dfbodenheizung is highly overall correlated with ConstructionYearHigh correlation
kapitalanlage is highly overall correlated with RoomsHigh correlation
kfw55 is highly overall correlated with erstbezug and 1 other fieldsHigh correlation
luftwp is highly overall correlated with ConstructionYear and 1 other fieldsHigh correlation
neubau is highly overall correlated with ConstructionYear and 3 other fieldsHigh correlation
personenaufzug is highly overall correlated with Object_priceHigh correlation
provisionsfrei is highly overall correlated with erstbezug and 2 other fieldsHigh correlation
sat is highly overall correlated with dslHigh correlation
wanne is highly overall correlated with dusche and 1 other fieldsHigh correlation
abrissobjekt is highly imbalanced (95.0%)Imbalance
alarmanlage is highly imbalanced (95.0%)Imbalance
als_ferienimmobilie_geeignet is highly imbalanced (91.1%)Imbalance
altbau_(bis_1945) is highly imbalanced (78.6%)Imbalance
aluminiumfenster is highly imbalanced (95.0%)Imbalance
bad/wc_getrennt is highly imbalanced (71.0%)Imbalance
barriefrei is highly imbalanced (64.2%)Imbalance
bidet is highly imbalanced (91.1%)Imbalance
carport is highly imbalanced (76.0%)Imbalance
dach_ausbauf\u00e4hig is highly imbalanced (76.0%)Imbalance
dach_ausgebaut is highly imbalanced (68.7%)Imbalance
denkmalgeschuetzt is highly imbalanced (91.1%)Imbalance
dielen is highly imbalanced (81.4%)Imbalance
dsl is highly imbalanced (76.0%)Imbalance
elektro is highly imbalanced (78.6%)Imbalance
erstbezug is highly imbalanced (68.7%)Imbalance
estrich is highly imbalanced (95.0%)Imbalance
etagenheizung is highly imbalanced (71.0%)Imbalance
fern is highly imbalanced (95.0%)Imbalance
ferne is highly imbalanced (58.1%)Imbalance
fertighaus is highly imbalanced (81.4%)Imbalance
fluessiggas is highly imbalanced (87.6%)Imbalance
fu\u00dfbodenheizung is highly imbalanced (58.1%)Imbalance
gartennutzung is highly imbalanced (91.1%)Imbalance
granit is highly imbalanced (95.0%)Imbalance
haustiere_erlaubt is highly imbalanced (73.4%)Imbalance
holz is highly imbalanced (81.4%)Imbalance
holzfenster is highly imbalanced (58.1%)Imbalance
kamera is highly imbalanced (95.0%)Imbalance
kapitalanlage is highly imbalanced (62.1%)Imbalance
kunststoff is highly imbalanced (81.4%)Imbalance
linoleum is highly imbalanced (68.7%)Imbalance
loggia is highly imbalanced (73.4%)Imbalance
luftwp is highly imbalanced (62.1%)Imbalance
moebliert is highly imbalanced (95.0%)Imbalance
neuwertig is highly imbalanced (84.4%)Imbalance
nicht_unterkellert is highly imbalanced (60.1%)Imbalance
ofenheizung is highly imbalanced (66.4%)Imbalance
offene_k\u00fcche is highly imbalanced (84.4%)Imbalance
pellet is highly imbalanced (87.6%)Imbalance
personenaufzug is highly imbalanced (95.0%)Imbalance
projektiert is highly imbalanced (60.1%)Imbalance
renoviert is highly imbalanced (78.6%)Imbalance
rollstuhlgerecht is highly imbalanced (87.6%)Imbalance
sat is highly imbalanced (68.7%)Imbalance
sauna is highly imbalanced (64.2%)Imbalance
speisekammer is highly imbalanced (73.4%)Imbalance
stein is highly imbalanced (81.4%)Imbalance
swimmingpool is highly imbalanced (78.6%)Imbalance
teil_saniert is highly imbalanced (87.6%)Imbalance
teilweise_m\u00f6bliert is highly imbalanced (87.6%)Imbalance
tiefgarage is highly imbalanced (91.1%)Imbalance
vollerschlossen is highly imbalanced (73.4%)Imbalance
wasch_trockenraum is highly imbalanced (60.1%)Imbalance
wg_geeignet is highly imbalanced (91.1%)Imbalance
wintergarten is highly imbalanced (78.6%)Imbalance
ConstructionYear has 31 (17.5%) missing valuesMissing
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique

Reproduction

Analysis started2023-07-12 15:00:34.392126
Analysis finished2023-07-12 15:01:17.554923
Duration43.16 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct177
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88
Minimum0
Maximum176
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-07-12T17:01:17.814341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.8
Q144
median88
Q3132
95-th percentile167.2
Maximum176
Range176
Interquartile range (IQR)88

Descriptive statistics

Standard deviation51.239633
Coefficient of variation (CV)0.58226856
Kurtosis-1.2
Mean88
Median Absolute Deviation (MAD)44
Skewness0
Sum15576
Variance2625.5
MonotonicityStrictly increasing
2023-07-12T17:01:18.082704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.6%
89 1
 
0.6%
113 1
 
0.6%
114 1
 
0.6%
115 1
 
0.6%
116 1
 
0.6%
117 1
 
0.6%
118 1
 
0.6%
119 1
 
0.6%
120 1
 
0.6%
Other values (167) 167
94.4%
ValueCountFrequency (%)
0 1
0.6%
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
ValueCountFrequency (%)
176 1
0.6%
175 1
0.6%
174 1
0.6%
173 1
0.6%
172 1
0.6%
171 1
0.6%
170 1
0.6%
169 1
0.6%
168 1
0.6%
167 1
0.6%

Object_price
Real number (ℝ)

HIGH CORRELATION 

Distinct136
Distinct (%)76.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean722367.98
Minimum154000
Maximum4995000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-07-12T17:01:18.342854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum154000
5-th percentile259800
Q1429000
median550000
Q3785000
95-th percentile1818000
Maximum4995000
Range4841000
Interquartile range (IQR)356000

Descriptive statistics

Standard deviation594211.68
Coefficient of variation (CV)0.82258863
Kurtosis18.64759
Mean722367.98
Median Absolute Deviation (MAD)151000
Skewness3.6918199
Sum1.2785913 × 108
Variance3.5308752 × 1011
MonotonicityNot monotonic
2023-07-12T17:01:18.583899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450000 5
 
2.8%
595000 5
 
2.8%
1250000 4
 
2.3%
550000 4
 
2.3%
499000 4
 
2.3%
695000 3
 
1.7%
690000 3
 
1.7%
375000 3
 
1.7%
360000 3
 
1.7%
399000 3
 
1.7%
Other values (126) 140
79.1%
ValueCountFrequency (%)
154000 1
0.6%
170000 1
0.6%
180000 1
0.6%
220000 1
0.6%
229000 1
0.6%
249000 1
0.6%
250000 1
0.6%
259000 2
1.1%
260000 1
0.6%
265000 1
0.6%
ValueCountFrequency (%)
4995000 1
0.6%
3490000 1
0.6%
2850000 1
0.6%
2700000 1
0.6%
2500000 2
1.1%
1998000 2
1.1%
1890000 1
0.6%
1800000 1
0.6%
1700000 1
0.6%
1600000 1
0.6%

LivingSpace
Real number (ℝ)

HIGH CORRELATION 

Distinct126
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean207.33876
Minimum66
Maximum1605
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-07-12T17:01:18.883333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum66
5-th percentile99.4
Q1137
median171
Q3229
95-th percentile399.6
Maximum1605
Range1539
Interquartile range (IQR)92

Descriptive statistics

Standard deviation155.07422
Coefficient of variation (CV)0.74792684
Kurtosis42.564323
Mean207.33876
Median Absolute Deviation (MAD)41
Skewness5.6136812
Sum36698.96
Variance24048.014
MonotonicityNot monotonic
2023-07-12T17:01:19.097789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130 4
 
2.3%
143 4
 
2.3%
140 4
 
2.3%
185 4
 
2.3%
137 4
 
2.3%
128 3
 
1.7%
225 3
 
1.7%
172.45 3
 
1.7%
135 3
 
1.7%
245 3
 
1.7%
Other values (116) 142
80.2%
ValueCountFrequency (%)
66 1
0.6%
80 1
0.6%
86 1
0.6%
87 1
0.6%
89 1
0.6%
91.5 1
0.6%
92 1
0.6%
96 1
0.6%
97 1
0.6%
100 2
1.1%
ValueCountFrequency (%)
1605 1
0.6%
1058 1
0.6%
740 1
0.6%
654 1
0.6%
520 1
0.6%
450 1
0.6%
413 2
1.1%
406 1
0.6%
398 1
0.6%
395 1
0.6%

Rooms
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2740113
Minimum2
Maximum107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-07-12T17:01:19.327261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q38
95-th percentile19.2
Maximum107
Range105
Interquartile range (IQR)3

Descriptive statistics

Standard deviation9.0023546
Coefficient of variation (CV)1.0880278
Kurtosis82.986493
Mean8.2740113
Median Absolute Deviation (MAD)1
Skewness7.989731
Sum1464.5
Variance81.042389
MonotonicityNot monotonic
2023-07-12T17:01:19.692472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
5 37
20.9%
7 31
17.5%
6 22
12.4%
4 20
11.3%
8 11
 
6.2%
10 10
 
5.6%
11 6
 
3.4%
3 5
 
2.8%
4.5 5
 
2.8%
18 4
 
2.3%
Other values (19) 26
14.7%
ValueCountFrequency (%)
2 1
 
0.6%
3 5
 
2.8%
4 20
11.3%
4.5 5
 
2.8%
5 37
20.9%
5.5 2
 
1.1%
6 22
12.4%
7 31
17.5%
7.5 2
 
1.1%
8 11
 
6.2%
ValueCountFrequency (%)
107 1
 
0.6%
34 1
 
0.6%
30 1
 
0.6%
28 1
 
0.6%
27 1
 
0.6%
22 1
 
0.6%
21 2
1.1%
20 1
 
0.6%
19 1
 
0.6%
18 4
2.3%

ConstructionYear
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct67
Distinct (%)45.9%
Missing31
Missing (%)17.5%
Infinite0
Infinite (%)0.0%
Mean1965.863
Minimum1780
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-07-12T17:01:19.884765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1780
5-th percentile1920
Q11955
median1969
Q31980
95-th percentile2015
Maximum2024
Range244
Interquartile range (IQR)25

Descriptive statistics

Standard deviation30.048611
Coefficient of variation (CV)0.015285201
Kurtosis9.3971587
Mean1965.863
Median Absolute Deviation (MAD)12
Skewness-1.7362291
Sum287016
Variance902.91904
MonotonicityNot monotonic
2023-07-12T17:01:20.115621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1960 7
 
4.0%
1974 7
 
4.0%
1967 7
 
4.0%
1920 7
 
4.0%
1980 6
 
3.4%
1978 5
 
2.8%
1950 5
 
2.8%
1979 4
 
2.3%
1969 4
 
2.3%
1966 4
 
2.3%
Other values (57) 90
50.8%
(Missing) 31
 
17.5%
ValueCountFrequency (%)
1780 1
 
0.6%
1897 1
 
0.6%
1900 1
 
0.6%
1907 1
 
0.6%
1910 2
 
1.1%
1912 1
 
0.6%
1920 7
4.0%
1927 1
 
0.6%
1930 2
 
1.1%
1933 1
 
0.6%
ValueCountFrequency (%)
2024 2
1.1%
2023 2
1.1%
2021 1
0.6%
2018 2
1.1%
2016 1
0.6%
2012 2
1.1%
2010 1
0.6%
2004 1
0.6%
1998 1
0.6%
1996 1
0.6%

ZipCode
Real number (ℝ)

Distinct26
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97162.52
Minimum97070
Maximum97299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-07-12T17:01:20.310117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum97070
5-th percentile97072
Q197078
median97204
Q397230
95-th percentile97299
Maximum97299
Range229
Interquartile range (IQR)152

Descriptive statistics

Standard deviation86.964144
Coefficient of variation (CV)0.00089503797
Kurtosis-1.6452146
Mean97162.52
Median Absolute Deviation (MAD)95
Skewness0.21923799
Sum17197766
Variance7562.7624
MonotonicityNot monotonic
2023-07-12T17:01:20.483845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
97080 20
 
11.3%
97078 17
 
9.6%
97204 14
 
7.9%
97076 13
 
7.3%
97299 11
 
6.2%
97209 11
 
6.2%
97084 10
 
5.6%
97082 10
 
5.6%
97222 9
 
5.1%
97297 7
 
4.0%
Other values (16) 55
31.1%
ValueCountFrequency (%)
97070 6
 
3.4%
97072 6
 
3.4%
97074 5
 
2.8%
97076 13
7.3%
97078 17
9.6%
97080 20
11.3%
97082 10
5.6%
97084 10
5.6%
97204 14
7.9%
97209 11
6.2%
ValueCountFrequency (%)
97299 11
6.2%
97297 7
4.0%
97295 1
 
0.6%
97288 1
 
0.6%
97286 1
 
0.6%
97276 1
 
0.6%
97270 3
 
1.7%
97265 6
3.4%
97261 4
 
2.3%
97249 2
 
1.1%

EstateType
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
HOUSE
177 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters885
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHOUSE
2nd rowHOUSE
3rd rowHOUSE
4th rowHOUSE
5th rowHOUSE

Common Values

ValueCountFrequency (%)
HOUSE 177
100.0%

Length

2023-07-12T17:01:20.664582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:20.842604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
house 177
100.0%

Most occurring characters

ValueCountFrequency (%)
H 177
20.0%
O 177
20.0%
U 177
20.0%
S 177
20.0%
E 177
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 885
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H 177
20.0%
O 177
20.0%
U 177
20.0%
S 177
20.0%
E 177
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 885
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
H 177
20.0%
O 177
20.0%
U 177
20.0%
S 177
20.0%
E 177
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 885
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H 177
20.0%
O 177
20.0%
U 177
20.0%
S 177
20.0%
E 177
20.0%

DistributionType
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
BUY
177 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters531
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBUY
2nd rowBUY
3rd rowBUY
4th rowBUY
5th rowBUY

Common Values

ValueCountFrequency (%)
BUY 177
100.0%

Length

2023-07-12T17:01:20.980479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:21.141313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
buy 177
100.0%

Most occurring characters

ValueCountFrequency (%)
B 177
33.3%
U 177
33.3%
Y 177
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 531
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 177
33.3%
U 177
33.3%
Y 177
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 531
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 177
33.3%
U 177
33.3%
Y 177
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 531
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 177
33.3%
U 177
33.3%
Y 177
33.3%

abrissobjekt
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
176 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Length

2023-07-12T17:01:21.273929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:21.433866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

abstellraum
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
146 
1
31 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 146
82.5%
1 31
 
17.5%

Length

2023-07-12T17:01:21.577180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:21.747085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 146
82.5%
1 31
 
17.5%

Most occurring characters

ValueCountFrequency (%)
0 146
82.5%
1 31
 
17.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 146
82.5%
1 31
 
17.5%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 146
82.5%
1 31
 
17.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 146
82.5%
1 31
 
17.5%

alarmanlage
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
176 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Length

2023-07-12T17:01:21.892031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:22.054853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

als_ferienimmobilie_geeignet
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
175 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Length

2023-07-12T17:01:22.194459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:22.372418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

altbau_(bis_1945)
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
171 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Length

2023-07-12T17:01:22.544503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:22.738727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

aluminiumfenster
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
176 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Length

2023-07-12T17:01:22.902646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:23.089122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

bad/wc_getrennt
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
168 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 168
94.9%
1 9
 
5.1%

Length

2023-07-12T17:01:23.237786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:23.443534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 168
94.9%
1 9
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0 168
94.9%
1 9
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 168
94.9%
1 9
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 168
94.9%
1 9
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 168
94.9%
1 9
 
5.1%

balkon
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
106 
1
71 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 106
59.9%
1 71
40.1%

Length

2023-07-12T17:01:23.578985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:23.745303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 106
59.9%
1 71
40.1%

Most occurring characters

ValueCountFrequency (%)
0 106
59.9%
1 71
40.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106
59.9%
1 71
40.1%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106
59.9%
1 71
40.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106
59.9%
1 71
40.1%

barriefrei
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
165 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 165
93.2%
1 12
 
6.8%

Length

2023-07-12T17:01:23.886033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:24.046338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 165
93.2%
1 12
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 165
93.2%
1 12
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 165
93.2%
1 12
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 165
93.2%
1 12
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 165
93.2%
1 12
 
6.8%

bidet
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
175 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Length

2023-07-12T17:01:24.182626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:24.345555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

carport
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
170 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 170
96.0%
1 7
 
4.0%

Length

2023-07-12T17:01:24.494802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:24.705699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 170
96.0%
1 7
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 170
96.0%
1 7
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 170
96.0%
1 7
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 170
96.0%
1 7
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 170
96.0%
1 7
 
4.0%

dach_ausbauf\u00e4hig
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
170 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 170
96.0%
1 7
 
4.0%

Length

2023-07-12T17:01:24.854080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:25.038511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 170
96.0%
1 7
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 170
96.0%
1 7
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 170
96.0%
1 7
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 170
96.0%
1 7
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 170
96.0%
1 7
 
4.0%

dach_ausgebaut
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
167 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

Length

2023-07-12T17:01:25.204982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:25.375157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

denkmalgeschuetzt
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
175 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Length

2023-07-12T17:01:25.510031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:25.709762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

dielen
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
172 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Length

2023-07-12T17:01:25.854289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:26.019569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

dsl
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
170 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 170
96.0%
1 7
 
4.0%

Length

2023-07-12T17:01:26.157259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:26.316358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 170
96.0%
1 7
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 170
96.0%
1 7
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 170
96.0%
1 7
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 170
96.0%
1 7
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 170
96.0%
1 7
 
4.0%

dusche
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
111 
1
66 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111
62.7%
1 66
37.3%

Length

2023-07-12T17:01:26.455355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:26.619933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 111
62.7%
1 66
37.3%

Most occurring characters

ValueCountFrequency (%)
0 111
62.7%
1 66
37.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 111
62.7%
1 66
37.3%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 111
62.7%
1 66
37.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 111
62.7%
1 66
37.3%

einbauk\u00fcche
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
132 
1
45 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 132
74.6%
1 45
 
25.4%

Length

2023-07-12T17:01:26.764910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:26.927569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 132
74.6%
1 45
 
25.4%

Most occurring characters

ValueCountFrequency (%)
0 132
74.6%
1 45
 
25.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 132
74.6%
1 45
 
25.4%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 132
74.6%
1 45
 
25.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 132
74.6%
1 45
 
25.4%

elektro
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
171 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Length

2023-07-12T17:01:27.062638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:27.224451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

erstbezug
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
167 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

Length

2023-07-12T17:01:27.357310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:27.515942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

estrich
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
176 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Length

2023-07-12T17:01:27.657585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:27.815763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

etagenheizung
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
168 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 168
94.9%
1 9
 
5.1%

Length

2023-07-12T17:01:27.950807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:28.117371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 168
94.9%
1 9
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0 168
94.9%
1 9
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 168
94.9%
1 9
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 168
94.9%
1 9
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 168
94.9%
1 9
 
5.1%

fenster
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
111 
1
66 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111
62.7%
1 66
37.3%

Length

2023-07-12T17:01:28.253940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:28.420105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 111
62.7%
1 66
37.3%

Most occurring characters

ValueCountFrequency (%)
0 111
62.7%
1 66
37.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 111
62.7%
1 66
37.3%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 111
62.7%
1 66
37.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 111
62.7%
1 66
37.3%

fern
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
176 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Length

2023-07-12T17:01:28.556905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:28.725474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

ferne
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
162 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 162
91.5%
1 15
 
8.5%

Length

2023-07-12T17:01:28.857675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:29.201501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 162
91.5%
1 15
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 162
91.5%
1 15
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 162
91.5%
1 15
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 162
91.5%
1 15
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 162
91.5%
1 15
 
8.5%

fertighaus
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
172 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Length

2023-07-12T17:01:29.342338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:29.505869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

fliesen
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
130 
1
47 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 130
73.4%
1 47
 
26.6%

Length

2023-07-12T17:01:29.641530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:29.806387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 130
73.4%
1 47
 
26.6%

Most occurring characters

ValueCountFrequency (%)
0 130
73.4%
1 47
 
26.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 130
73.4%
1 47
 
26.6%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 130
73.4%
1 47
 
26.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 130
73.4%
1 47
 
26.6%

fluessiggas
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
174 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Length

2023-07-12T17:01:29.945698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:30.109656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

frei
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
156 
1
21 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 156
88.1%
1 21
 
11.9%

Length

2023-07-12T17:01:30.244957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:30.409039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 156
88.1%
1 21
 
11.9%

Most occurring characters

ValueCountFrequency (%)
0 156
88.1%
1 21
 
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 156
88.1%
1 21
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 156
88.1%
1 21
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 156
88.1%
1 21
 
11.9%

fu\u00dfbodenheizung
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
162 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 162
91.5%
1 15
 
8.5%

Length

2023-07-12T17:01:30.548447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:30.711116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 162
91.5%
1 15
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 162
91.5%
1 15
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 162
91.5%
1 15
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 162
91.5%
1 15
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 162
91.5%
1 15
 
8.5%

gaestewc
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
1
96 
0
81 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 96
54.2%
0 81
45.8%

Length

2023-07-12T17:01:30.849999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:31.012788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 96
54.2%
0 81
45.8%

Most occurring characters

ValueCountFrequency (%)
1 96
54.2%
0 81
45.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 96
54.2%
0 81
45.8%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 96
54.2%
0 81
45.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 96
54.2%
0 81
45.8%

garage
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
117 
1
60 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 117
66.1%
1 60
33.9%

Length

2023-07-12T17:01:31.150877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:31.318945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 117
66.1%
1 60
33.9%

Most occurring characters

ValueCountFrequency (%)
0 117
66.1%
1 60
33.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 117
66.1%
1 60
33.9%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 117
66.1%
1 60
33.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 117
66.1%
1 60
33.9%

garten
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
90 
1
87 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 90
50.8%
1 87
49.2%

Length

2023-07-12T17:01:31.456291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:31.622212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 90
50.8%
1 87
49.2%

Most occurring characters

ValueCountFrequency (%)
0 90
50.8%
1 87
49.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 90
50.8%
1 87
49.2%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 90
50.8%
1 87
49.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 90
50.8%
1 87
49.2%

gartennutzung
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
175 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Length

2023-07-12T17:01:31.761946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:31.923079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

gas
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
101 
1
76 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 101
57.1%
1 76
42.9%

Length

2023-07-12T17:01:32.060778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:32.221213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 101
57.1%
1 76
42.9%

Most occurring characters

ValueCountFrequency (%)
0 101
57.1%
1 76
42.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 101
57.1%
1 76
42.9%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 101
57.1%
1 76
42.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 101
57.1%
1 76
42.9%

gepflegt
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
137 
1
40 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 137
77.4%
1 40
 
22.6%

Length

2023-07-12T17:01:32.361162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:32.524426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 137
77.4%
1 40
 
22.6%

Most occurring characters

ValueCountFrequency (%)
0 137
77.4%
1 40
 
22.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 137
77.4%
1 40
 
22.6%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 137
77.4%
1 40
 
22.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 137
77.4%
1 40
 
22.6%

granit
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
176 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Length

2023-07-12T17:01:32.669995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:32.829266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

haustiere_erlaubt
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
169 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

Length

2023-07-12T17:01:32.964588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:33.129887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

holz
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
172 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Length

2023-07-12T17:01:33.262519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:33.426576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

holzfenster
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
162 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 162
91.5%
1 15
 
8.5%

Length

2023-07-12T17:01:33.561818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:33.730057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 162
91.5%
1 15
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 162
91.5%
1 15
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 162
91.5%
1 15
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 162
91.5%
1 15
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 162
91.5%
1 15
 
8.5%

kable_sat_tv
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
154 
1
23 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 154
87.0%
1 23
 
13.0%

Length

2023-07-12T17:01:33.868909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:34.039479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 154
87.0%
1 23
 
13.0%

Most occurring characters

ValueCountFrequency (%)
0 154
87.0%
1 23
 
13.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 154
87.0%
1 23
 
13.0%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 154
87.0%
1 23
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 154
87.0%
1 23
 
13.0%

kamera
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
176 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Length

2023-07-12T17:01:34.177285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:34.339017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

kamin
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
157 
1
20 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 157
88.7%
1 20
 
11.3%

Length

2023-07-12T17:01:34.474573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:34.636531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 157
88.7%
1 20
 
11.3%

Most occurring characters

ValueCountFrequency (%)
0 157
88.7%
1 20
 
11.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 157
88.7%
1 20
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 157
88.7%
1 20
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 157
88.7%
1 20
 
11.3%

kapitalanlage
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
164 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 164
92.7%
1 13
 
7.3%

Length

2023-07-12T17:01:34.783153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:34.944259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 164
92.7%
1 13
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 164
92.7%
1 13
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 164
92.7%
1 13
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 164
92.7%
1 13
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 164
92.7%
1 13
 
7.3%

kfw55
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
157 
1
20 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 157
88.7%
1 20
 
11.3%

Length

2023-07-12T17:01:35.080543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:35.243802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 157
88.7%
1 20
 
11.3%

Most occurring characters

ValueCountFrequency (%)
0 157
88.7%
1 20
 
11.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 157
88.7%
1 20
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 157
88.7%
1 20
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 157
88.7%
1 20
 
11.3%

kunststoff
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
172 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Length

2023-07-12T17:01:35.381556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:35.546061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%
Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
151 
1
26 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 151
85.3%
1 26
 
14.7%

Length

2023-07-12T17:01:35.682278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:35.845123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 151
85.3%
1 26
 
14.7%

Most occurring characters

ValueCountFrequency (%)
0 151
85.3%
1 26
 
14.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 151
85.3%
1 26
 
14.7%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 151
85.3%
1 26
 
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 151
85.3%
1 26
 
14.7%

laminat
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
152 
1
25 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 152
85.9%
1 25
 
14.1%

Length

2023-07-12T17:01:35.982113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:36.147838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 152
85.9%
1 25
 
14.1%

Most occurring characters

ValueCountFrequency (%)
0 152
85.9%
1 25
 
14.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 152
85.9%
1 25
 
14.1%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 152
85.9%
1 25
 
14.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 152
85.9%
1 25
 
14.1%

linoleum
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
167 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

Length

2023-07-12T17:01:36.286171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:36.453289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

loggia
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
169 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

Length

2023-07-12T17:01:36.588516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:36.752814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

luftwp
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
164 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 164
92.7%
1 13
 
7.3%

Length

2023-07-12T17:01:36.887113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:37.045298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 164
92.7%
1 13
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 164
92.7%
1 13
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 164
92.7%
1 13
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 164
92.7%
1 13
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 164
92.7%
1 13
 
7.3%

massivhaus
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
136 
1
41 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 136
76.8%
1 41
 
23.2%

Length

2023-07-12T17:01:37.179533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:37.342550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 136
76.8%
1 41
 
23.2%

Most occurring characters

ValueCountFrequency (%)
0 136
76.8%
1 41
 
23.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 136
76.8%
1 41
 
23.2%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 136
76.8%
1 41
 
23.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 136
76.8%
1 41
 
23.2%

moebliert
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
176 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Length

2023-07-12T17:01:37.482089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:37.646409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

neubau
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
156 
1
21 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 156
88.1%
1 21
 
11.9%

Length

2023-07-12T17:01:37.785027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:37.949710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 156
88.1%
1 21
 
11.9%

Most occurring characters

ValueCountFrequency (%)
0 156
88.1%
1 21
 
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 156
88.1%
1 21
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 156
88.1%
1 21
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 156
88.1%
1 21
 
11.9%

neuwertig
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
173 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 173
97.7%
1 4
 
2.3%

Length

2023-07-12T17:01:38.095470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:38.257467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 173
97.7%
1 4
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 173
97.7%
1 4
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 173
97.7%
1 4
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 173
97.7%
1 4
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 173
97.7%
1 4
 
2.3%

nicht_unterkellert
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
163 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 163
92.1%
1 14
 
7.9%

Length

2023-07-12T17:01:38.391776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:38.554267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 163
92.1%
1 14
 
7.9%

Most occurring characters

ValueCountFrequency (%)
0 163
92.1%
1 14
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 163
92.1%
1 14
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 163
92.1%
1 14
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 163
92.1%
1 14
 
7.9%

oel
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
136 
1
41 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 136
76.8%
1 41
 
23.2%

Length

2023-07-12T17:01:38.686227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:39.027148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 136
76.8%
1 41
 
23.2%

Most occurring characters

ValueCountFrequency (%)
0 136
76.8%
1 41
 
23.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 136
76.8%
1 41
 
23.2%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 136
76.8%
1 41
 
23.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 136
76.8%
1 41
 
23.2%

ofenheizung
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
166 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 166
93.8%
1 11
 
6.2%

Length

2023-07-12T17:01:39.165560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:39.327204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 166
93.8%
1 11
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 166
93.8%
1 11
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 166
93.8%
1 11
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 166
93.8%
1 11
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 166
93.8%
1 11
 
6.2%

offene_k\u00fcche
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
173 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 173
97.7%
1 4
 
2.3%

Length

2023-07-12T17:01:39.466324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:39.629530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 173
97.7%
1 4
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 173
97.7%
1 4
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 173
97.7%
1 4
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 173
97.7%
1 4
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 173
97.7%
1 4
 
2.3%

parkett
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
148 
1
29 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 148
83.6%
1 29
 
16.4%

Length

2023-07-12T17:01:39.765347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:39.927036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 148
83.6%
1 29
 
16.4%

Most occurring characters

ValueCountFrequency (%)
0 148
83.6%
1 29
 
16.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 148
83.6%
1 29
 
16.4%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 148
83.6%
1 29
 
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 148
83.6%
1 29
 
16.4%

pellet
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
174 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Length

2023-07-12T17:01:40.076872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:40.312903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

personenaufzug
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
176 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Length

2023-07-12T17:01:40.470369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:40.636849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 176
99.4%
1 1
 
0.6%

projektiert
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
163 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 163
92.1%
1 14
 
7.9%

Length

2023-07-12T17:01:40.775829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:40.944764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 163
92.1%
1 14
 
7.9%

Most occurring characters

ValueCountFrequency (%)
0 163
92.1%
1 14
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 163
92.1%
1 14
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 163
92.1%
1 14
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 163
92.1%
1 14
 
7.9%

provisionsfrei
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
148 
1
29 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 148
83.6%
1 29
 
16.4%

Length

2023-07-12T17:01:41.081102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:41.252850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 148
83.6%
1 29
 
16.4%

Most occurring characters

ValueCountFrequency (%)
0 148
83.6%
1 29
 
16.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 148
83.6%
1 29
 
16.4%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 148
83.6%
1 29
 
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 148
83.6%
1 29
 
16.4%

renoviert
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
171 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Length

2023-07-12T17:01:41.394216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:41.559134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%
Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
155 
1
22 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 155
87.6%
1 22
 
12.4%

Length

2023-07-12T17:01:41.696666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:41.858404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 155
87.6%
1 22
 
12.4%

Most occurring characters

ValueCountFrequency (%)
0 155
87.6%
1 22
 
12.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 155
87.6%
1 22
 
12.4%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 155
87.6%
1 22
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 155
87.6%
1 22
 
12.4%

rollstuhlgerecht
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
174 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Length

2023-07-12T17:01:42.031096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:42.222799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

sat
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
167 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

Length

2023-07-12T17:01:42.389808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:42.574586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 167
94.4%
1 10
 
5.6%

sauna
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
165 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 165
93.2%
1 12
 
6.8%

Length

2023-07-12T17:01:42.721969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:42.904938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 165
93.2%
1 12
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 165
93.2%
1 12
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 165
93.2%
1 12
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 165
93.2%
1 12
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 165
93.2%
1 12
 
6.8%

speisekammer
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
169 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

Length

2023-07-12T17:01:43.042985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:43.239792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

stein
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
172 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Length

2023-07-12T17:01:43.424586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:44.272597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 172
97.2%
1 5
 
2.8%

stellplatz
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
123 
1
54 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 123
69.5%
1 54
30.5%

Length

2023-07-12T17:01:44.904164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:45.141416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 123
69.5%
1 54
30.5%

Most occurring characters

ValueCountFrequency (%)
0 123
69.5%
1 54
30.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 123
69.5%
1 54
30.5%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 123
69.5%
1 54
30.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 123
69.5%
1 54
30.5%

swimmingpool
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
171 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Length

2023-07-12T17:01:45.322596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:45.537122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

teil_saniert
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
174 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Length

2023-07-12T17:01:45.758596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:45.941715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

teilweise_m\u00f6bliert
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
174 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Length

2023-07-12T17:01:46.086577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:46.255575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
98.3%
1 3
 
1.7%

teppich
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
148 
1
29 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 148
83.6%
1 29
 
16.4%

Length

2023-07-12T17:01:46.439096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:46.713262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 148
83.6%
1 29
 
16.4%

Most occurring characters

ValueCountFrequency (%)
0 148
83.6%
1 29
 
16.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 148
83.6%
1 29
 
16.4%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 148
83.6%
1 29
 
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 148
83.6%
1 29
 
16.4%

terrasse
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
1
96 
0
81 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 96
54.2%
0 81
45.8%

Length

2023-07-12T17:01:46.930524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:47.195336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 96
54.2%
0 81
45.8%

Most occurring characters

ValueCountFrequency (%)
1 96
54.2%
0 81
45.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 96
54.2%
0 81
45.8%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 96
54.2%
0 81
45.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 96
54.2%
0 81
45.8%

tiefgarage
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
175 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Length

2023-07-12T17:01:47.402269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:47.658358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

unterkellert
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
1
95 
0
82 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 95
53.7%
0 82
46.3%

Length

2023-07-12T17:01:47.878597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:48.145330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 95
53.7%
0 82
46.3%

Most occurring characters

ValueCountFrequency (%)
1 95
53.7%
0 82
46.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 95
53.7%
0 82
46.3%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 95
53.7%
0 82
46.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 95
53.7%
0 82
46.3%

vermietet
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
147 
1
30 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 147
83.1%
1 30
 
16.9%

Length

2023-07-12T17:01:48.330617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:48.544493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 147
83.1%
1 30
 
16.9%

Most occurring characters

ValueCountFrequency (%)
0 147
83.1%
1 30
 
16.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147
83.1%
1 30
 
16.9%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 147
83.1%
1 30
 
16.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 147
83.1%
1 30
 
16.9%

vollerschlossen
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
169 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

Length

2023-07-12T17:01:48.705935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:48.902803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 169
95.5%
1 8
 
4.5%

wanne
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
97 
1
80 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 97
54.8%
1 80
45.2%

Length

2023-07-12T17:01:49.096366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:49.291747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 97
54.8%
1 80
45.2%

Most occurring characters

ValueCountFrequency (%)
0 97
54.8%
1 80
45.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97
54.8%
1 80
45.2%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97
54.8%
1 80
45.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97
54.8%
1 80
45.2%

wasch_trockenraum
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
163 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 163
92.1%
1 14
 
7.9%

Length

2023-07-12T17:01:49.458563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:49.638564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 163
92.1%
1 14
 
7.9%

Most occurring characters

ValueCountFrequency (%)
0 163
92.1%
1 14
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 163
92.1%
1 14
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 163
92.1%
1 14
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 163
92.1%
1 14
 
7.9%

wg_geeignet
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
175 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Length

2023-07-12T17:01:49.781862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:49.962636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
98.9%
1 2
 
1.1%

wintergarten
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
0
171 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Length

2023-07-12T17:01:50.171232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:50.444337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 171
96.6%
1 6
 
3.4%

zentralheizung
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
1
104 
0
73 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 104
58.8%
0 73
41.2%

Length

2023-07-12T17:01:50.606961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-12T17:01:50.787191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 104
58.8%
0 73
41.2%

Most occurring characters

ValueCountFrequency (%)
1 104
58.8%
0 73
41.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 104
58.8%
0 73
41.2%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 104
58.8%
0 73
41.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 104
58.8%
0 73
41.2%

Interactions

2023-07-12T17:01:13.735917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:08.775824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:09.763283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:10.721580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:11.709210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:12.655225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:13.907170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:08.940909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:09.940846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:10.906231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:11.864802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:12.965062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:14.048446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:09.091771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:10.092521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:11.079198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:12.010895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:13.108572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:14.210677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:09.272519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:10.248717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:11.251360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:12.195891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:13.274453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:14.348449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:09.436324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:10.413484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:11.402107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:12.364938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:13.446681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:14.482170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:09.607229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:10.570542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:11.551500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:12.513685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-12T17:01:13.582452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-12T17:01:51.351775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Unnamed: 0Object_priceLivingSpaceRoomsConstructionYearZipCodeabrissobjektabstellraumalarmanlageals_ferienimmobilie_geeignetaltbau_(bis_1945)aluminiumfensterbad/wc_getrenntbalkonbarriefreibidetcarportdach_ausbauf\u00e4higdach_ausgebautdenkmalgeschuetztdielendslduscheeinbauk\u00fccheelektroerstbezugestrichetagenheizungfensterfernfernefertighausfliesenfluessiggasfreifu\u00dfbodenheizunggaestewcgaragegartengartennutzunggasgepflegtgranithaustiere_erlaubtholzholzfensterkable_sat_tvkamerakaminkapitalanlagekfw55kunststoffkunststofffensterlaminatlinoleumloggialuftwpmassivhausmoebliertneubauneuwertignicht_unterkellertoelofenheizungoffene_k\u00fccheparkettpelletpersonenaufzugprojektiertprovisionsfreirenoviertrenovierungsbed\u00fcrftigrollstuhlgerechtsatsaunaspeisekammersteinstellplatzswimmingpoolteil_saniertteilweise_m\u00f6bliertteppichterrassetiefgarageunterkellertvermietetvollerschlossenwannewasch_trockenraumwg_geeignetwintergartenzentralheizung
Unnamed: 01.000-0.201-0.079-0.0990.0020.1480.0000.0000.0490.0000.2360.0000.0000.0230.1290.0000.1270.0000.0000.0000.0000.0000.1140.0000.1860.2330.0490.0000.2260.0000.0790.0340.2530.0000.1050.0000.0000.1940.0000.0000.1690.0000.0000.0000.0000.0000.0000.0490.1070.2130.2360.0000.1560.0000.0000.0000.0000.0440.0000.0000.2070.1400.0590.0000.0000.0000.1610.0490.2010.2160.1090.1080.1740.0000.2140.0000.0500.1610.0000.0000.1650.2130.0870.0000.0920.0000.1460.2270.0000.2240.0000.000
Object_price-0.2011.0000.6620.4000.308-0.2610.0000.0000.3960.0000.2100.0000.3020.0000.1910.0000.0000.3250.0000.6850.4190.0000.1050.0000.0880.0500.0000.2940.1020.0000.0490.0000.0620.0000.2750.1730.1580.0000.0000.0000.1210.0000.0000.0000.0000.1180.0700.3960.2710.3180.1110.0000.1030.0000.0000.0000.0000.0470.0000.0000.3920.0000.0000.0000.0000.0000.0000.6780.0000.1370.3580.0000.1470.2870.3910.0000.1100.1820.4090.0000.1470.0000.0000.4550.1830.0000.0000.1820.0000.0490.0000.000
LivingSpace-0.0790.6621.0000.7450.126-0.0600.0000.0000.0000.0000.3610.0000.3030.0600.0000.0000.0000.3440.0000.6900.4130.0000.1360.0000.0000.0000.0000.4270.1460.0000.0000.0000.1360.0000.1350.0000.3180.1890.1710.0000.0000.0740.0000.0000.0000.0000.1130.0000.0000.4260.0380.0000.0930.0820.0000.0990.0000.0960.0000.0000.0840.0000.0000.2360.0000.1280.0000.3230.0000.0000.0000.0840.0000.2580.0000.0000.0000.0450.0730.0000.0000.1280.1430.1760.0000.2570.0000.2220.0000.0000.0000.000
Rooms-0.0990.4000.7451.000-0.0810.0340.2220.0000.0000.0000.1640.0000.2940.0000.0000.0000.0000.3450.2690.6910.4190.0730.0880.0000.0000.0000.0000.3220.0880.0000.2660.0000.0800.0000.1530.0000.2860.0700.1310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.5400.0000.0000.1190.1250.0000.0000.0000.1280.0000.0000.2330.0000.0000.1310.0000.1610.0000.0000.0000.0000.0000.0000.0000.2990.0000.0000.0000.0490.0000.0000.2590.1030.1350.0000.0000.2460.0000.1020.0640.0000.0000.000
ConstructionYear0.0020.3080.126-0.0811.000-0.1080.0000.2620.1890.0000.4110.0000.3380.3390.0000.0000.0000.1820.0000.1510.0820.0000.2680.1670.4741.0000.0000.0000.0890.0000.0000.1270.0000.0000.0000.5830.2550.2420.0000.0000.2860.1750.0000.0000.0000.0000.0000.1890.0000.0000.3380.0000.1220.1810.0000.0440.6120.0380.1890.5980.3580.3150.3220.0000.0000.0000.2890.0000.1890.1830.0000.1860.0000.0000.2850.0440.0000.0000.3310.0000.0000.1780.0000.0000.2880.0000.3000.1060.0000.1210.0000.211
ZipCode0.148-0.261-0.0600.034-0.1081.0000.0000.2210.0000.1530.0000.1180.1260.0460.0000.0460.0000.1730.0000.0000.0000.2210.0790.1100.0000.1210.0000.0000.0330.0000.0000.0000.0760.2350.1400.0000.1400.0000.0000.0000.2650.2460.0000.0000.2240.2270.1100.0000.0430.0000.1480.0000.1360.0000.0000.1450.1880.1240.2790.0000.3410.0000.3320.0000.0000.1590.1140.0000.1070.0000.0000.0000.0000.1700.0000.0000.0000.0000.0000.0000.0000.2020.0000.0000.0000.0000.0000.1100.0650.0000.0000.095
abrissobjekt0.0000.0000.0000.2220.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
abstellraum0.0000.0000.0000.0000.2620.2210.0001.0000.0000.0000.0000.0000.0000.2650.0000.0000.1570.0000.2300.0000.0000.2390.3600.2850.0000.0290.0000.0000.2970.0000.1930.0000.3040.0000.2580.0000.2170.1000.3580.1430.1370.0000.0000.0000.0000.0000.0000.0000.0000.0000.1190.1250.1940.1100.0840.1300.0000.0000.0000.1250.0000.0000.0000.0000.0270.1610.0000.0000.0770.1230.0000.0000.0000.0840.0000.3580.0000.0000.0000.0830.0830.3310.0800.0000.1910.0470.0000.3360.2680.0000.0000.105
alarmanlage0.0490.3960.0000.0000.1890.0000.0000.0001.0000.0000.0000.0000.0000.0000.1060.0000.1620.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0840.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4930.0530.0000.0000.0000.0000.0000.0000.0000.0980.0000.0000.0000.0000.0900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2730.0000.0000.0000.0000.0000.1790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0900.0000.0000.000
als_ferienimmobilie_geeignet0.0000.0000.0000.0000.0000.1530.0000.0000.0001.0000.0000.0000.0000.0100.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
altbau_(bis_1945)0.2360.2100.3610.1640.4110.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2110.0000.0000.0000.0000.0000.0000.0000.0000.0600.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1250.0000.0000.0000.0000.0000.0000.0000.0000.0600.0000.0000.0000.0000.0500.0000.0000.0000.0000.0000.0000.0000.0000.1690.0000.0000.0000.0000.000
aluminiumfenster0.0000.0000.0000.0000.0000.1180.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.1240.0000.0000.1620.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0840.0000.0000.0000.0980.0000.0000.0000.0190.1240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1240.1060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0900.0000.0000.000
bad/wc_getrennt0.0000.3020.3030.2940.3380.1260.0000.0000.0000.0000.0000.0001.0000.0650.0000.0000.0000.2730.0810.0610.0000.0000.0000.0000.1530.0000.0000.0000.0860.0000.0000.0000.0000.0000.1790.0000.0000.1720.0000.0000.0000.0500.0000.0000.0000.3380.0000.0000.0000.0340.0000.0000.2190.0000.0000.1130.0000.3220.0000.0000.0000.0000.0420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0810.0510.0000.1790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0610.0000.000
balkon0.0230.0000.0600.0000.3390.0460.0000.2650.0000.0100.0000.0000.0651.0000.0000.0000.0000.0000.0000.0000.0000.0000.1760.1830.0960.1590.0000.0810.1760.0000.0700.0000.1850.0000.0000.1250.0000.1920.0750.0000.1450.0580.0000.0000.0000.0000.0000.0000.1450.0000.2640.0000.0000.3050.0000.0000.1460.0000.0000.2730.0000.1100.0810.0520.0000.2990.0000.0000.2060.2420.0000.0000.0000.0000.0170.0000.0000.0600.0000.0880.0880.1320.1170.0000.3250.0750.0000.2050.0000.0000.0000.217
barriefrei0.1290.1910.0000.0000.0000.0000.0000.0000.1060.0180.0000.0000.0000.0001.0000.0000.0910.0000.0000.0180.0000.0000.0520.1020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1060.0000.0000.0300.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1050.0000.0000.0000.2130.0000.1310.0000.0000.0740.2490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
bidet0.0000.0000.0000.0000.0000.0460.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0880.0360.0960.0000.0000.0000.0000.0360.0000.0000.0000.0900.1780.0000.0000.0000.0000.0000.2300.0000.0000.0000.0000.1220.0000.0000.0000.0000.0000.0000.1220.0000.0000.0000.0000.0000.0000.0000.0000.1460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0740.0000.0710.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
carport0.1270.0000.0000.0000.0000.0000.0000.1570.1620.0000.0000.0000.0000.0000.0910.0001.0000.0000.0000.0000.0000.0000.0000.1650.0000.0000.0000.0000.0000.0000.0570.0000.0000.0000.0000.0570.0000.0000.1610.0000.1250.0000.1620.0000.2160.0570.0000.1620.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0680.0000.0000.0000.0680.0000.0000.000
dach_ausbauf\u00e4hig0.0000.3250.3440.3450.1820.1730.0000.0000.0000.0000.0000.0000.2730.0000.0000.0000.0001.0000.0000.0880.0000.0000.0850.0860.0000.0000.1620.1310.0850.0000.0570.0000.0000.0000.0000.0000.0000.1070.0000.0000.0000.0000.0000.0000.0000.1840.0000.0000.0000.0000.0000.0000.2750.0000.0000.1470.0000.1050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1170.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0680.0000.0190.0000.0000.0000.000
dach_ausgebaut0.0000.0000.0000.2690.0000.0000.0000.2300.0000.0000.0000.1240.0810.0000.0000.0000.0000.0001.0000.0000.0000.5110.1190.1490.0000.0000.0000.0000.1760.0000.4030.0000.1390.0000.1580.0000.0000.3080.1590.0490.0790.1080.0000.0000.1640.2210.0000.0000.0000.1480.0000.0000.2690.2780.0000.2300.0000.2310.0000.0000.0000.0000.0000.1750.0000.0000.0000.0000.0000.0000.0000.0000.0000.4110.0270.2300.0000.0000.0000.0000.2410.2450.0690.0000.1340.0000.0000.1260.4210.0000.1380.029
denkmalgeschuetzt0.0000.6850.6900.6910.1510.0000.0000.0000.0000.0000.0000.0000.0610.0000.0180.0000.0000.0880.0001.0000.4610.0000.0000.0000.0000.0000.0000.0610.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
dielen0.0000.4190.4130.4190.0820.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4611.0000.0000.0000.0600.0000.0000.0000.1790.0880.0000.0000.0000.1500.0000.0590.0000.0000.0000.0000.0000.0550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1530.0190.1640.0000.0000.0780.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1220.0000.000
dsl0.0000.0000.0000.0730.0000.2210.0000.2390.0000.0000.0000.1620.0000.0000.0000.0880.0000.0000.5110.0000.0001.0000.1560.0860.0000.0000.0000.0000.1560.0000.1840.0000.0770.0000.2280.0000.0650.2420.0930.0880.1910.0000.0000.0000.0000.1840.0000.0000.0000.0000.0000.2160.1880.1010.0000.1470.0000.2560.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.5110.0910.0000.0000.0000.0000.0000.0410.2520.0650.0000.0680.0680.0000.1140.3080.0000.0000.032
dusche0.1140.1050.1360.0880.2680.0790.0000.3600.0000.0000.0000.0000.0000.1760.0520.0360.0000.0850.1190.0000.0000.1561.0000.2780.0310.2830.0000.0000.6470.0000.0270.0880.2260.0000.1090.0000.3130.0000.2230.0360.0630.0660.0000.0000.0000.1460.0000.0000.0000.0000.1290.0880.2120.2290.1190.1830.1460.1230.0000.1510.0000.1430.0730.0000.1390.0390.0000.0000.1430.0000.0000.0000.0000.1760.0000.0400.0000.0000.0000.1000.0000.2640.0000.0000.1210.0710.1200.6940.0640.0000.0000.046
einbauk\u00fcche0.0000.0000.0000.0000.1670.1100.0000.2850.0000.0000.0000.0000.0000.1830.1020.0960.1650.0860.1490.0000.0600.0860.2781.0000.0000.0870.0000.0740.2780.0000.2050.0000.1110.0000.1490.0000.1090.1230.3930.0000.0000.1820.0000.0590.2420.0220.1050.0000.2090.0000.1730.0600.2420.1350.0000.2690.0490.0570.0000.1350.0000.0640.1370.0520.0000.0800.0000.0000.1270.0000.1200.0000.0000.0800.0000.2040.0000.1450.1200.0000.1580.1640.1400.0000.3670.0000.0000.2820.1740.0000.0000.255
elektro0.1860.0880.0000.0000.4740.0000.0000.0000.0000.0000.2110.0000.1530.0960.0000.0000.0000.0000.0000.0000.0000.0000.0310.0001.0000.0000.0000.0000.0310.0000.0000.2400.0000.0000.0000.0820.0000.0000.0000.0000.1070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1020.1370.0000.1560.0000.0000.0000.1250.0130.0000.0000.0000.0000.0000.0000.0000.0600.0000.0000.0000.0000.0500.0000.0000.0000.0000.0000.0000.0770.0000.1690.0000.0000.0000.0000.000
erstbezug0.2330.0500.0000.0001.0000.1210.0000.0290.0000.0000.0000.0000.0000.1590.0000.0000.0000.0000.0000.0000.0000.0000.2830.0870.0001.0000.0000.0000.1190.0000.0000.0000.0930.0000.0000.0000.1860.1290.2030.0000.1720.0700.0000.0000.0000.0000.0000.0000.0000.0000.6440.0000.0000.0000.0000.0000.4420.0740.0000.6260.0000.0000.0740.0000.0000.0000.0000.0000.0000.5160.0000.0000.0000.0000.0000.0000.0000.1130.0000.0000.0000.0000.2300.0000.2270.0200.0000.2340.0000.0000.0000.000
estrich0.0490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1620.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.2020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
etagenheizung0.0000.2940.4270.3220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0810.0000.0000.0000.1310.0000.0610.1790.0000.0000.0740.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0970.0000.0000.0000.1150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0660.0000.0000.0000.0000.0000.0000.0000.0000.0000.0810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0610.0000.0000.0000.0300.0000.0610.0000.239
fenster0.2260.1020.1460.0880.0890.0330.0000.2970.0000.0360.0000.0000.0860.1760.0000.0360.0000.0850.1760.0360.0880.1560.6470.2780.0310.1190.0000.0001.0000.0000.0000.0000.3360.0000.1510.0000.2890.0000.2970.0360.0000.0660.0000.0360.0880.1920.0000.0000.0790.0000.0000.0880.2470.2650.1190.1830.0000.2150.0000.0000.0000.0000.0000.0000.1390.0890.0000.0000.1900.0000.0000.0890.0000.0490.0000.1200.0000.0000.0000.0000.0000.2640.0430.0000.1990.0000.1200.6940.0640.0360.0000.046
fern0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
ferne0.0790.0490.0000.2660.0000.0000.0000.1930.0000.0000.0000.0000.0000.0700.0000.0000.0570.0570.4030.0000.0000.1840.0270.2050.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.3520.0000.0000.2200.1020.0000.0380.0000.0840.0000.1080.2230.0440.0000.0000.0000.0000.0000.1740.1820.0000.0280.0000.0000.0000.0000.0000.0000.0000.1080.0000.0000.0000.0000.0000.0000.0000.0000.0000.2210.0000.1620.0000.1050.0000.0000.1810.0000.0000.0000.1190.0750.0000.0820.2380.0000.0000.081
fertighaus0.0340.0000.0000.0000.1270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0880.0000.2400.0000.2020.0000.0000.0000.0001.0000.0500.0000.0000.1080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1280.0000.0000.0590.0000.0000.0000.0000.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0800.0000.0000.0000.0000.0000.0000.0390.1180.0000.0000.000
fliesen0.2530.0620.1360.0800.0000.0760.0000.3040.0000.0000.0000.0000.0000.1850.0000.0900.0000.0000.1390.0000.1500.0770.2260.1110.0000.0930.0000.0000.3360.0000.0000.0501.0000.0000.0000.0000.1050.0000.3090.0000.1150.0000.0000.0000.0500.0880.0000.0000.0000.0000.1350.0000.1480.3180.3730.0000.0000.1180.0000.0330.0000.0000.0000.0000.0000.3670.0000.0000.1330.1240.0170.2070.0000.0000.0000.0390.0000.0000.0000.1510.0000.4720.0170.0000.1430.0000.1260.3600.2140.0000.0000.067
fluessiggas0.0000.0000.0000.0000.0000.2350.0000.0000.0000.0000.0600.0000.0000.0000.0000.1780.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0600.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
frei0.1050.2750.1350.1530.0000.1400.0000.2580.0000.0000.0000.0000.1790.0000.0000.0000.0000.0000.1580.0000.0590.2280.1090.1490.0000.0000.0000.0000.1510.0000.3520.0000.0000.0001.0000.0000.1230.2240.1650.1950.1790.1130.0470.0000.0590.4800.0000.0000.0000.0000.0710.2980.3080.1030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2270.0000.0000.0000.0000.1170.0000.0000.0000.2400.0000.3760.0590.2990.0000.0000.0000.0000.1230.0000.0850.0000.0000.1980.1680.0000.0000.167
fu\u00dfbodenheizung0.0000.1730.0000.0000.5830.0000.0000.0000.0840.0000.0000.0000.0000.1250.0000.0000.0570.0000.0000.0000.0000.0000.0000.0000.0820.0000.0000.0000.0000.0000.0000.1080.0000.0000.0001.0000.1150.1340.0100.0000.0940.1190.0840.0000.1080.0000.0000.0840.0000.0000.0000.0000.0000.0570.0000.0000.4140.0580.0840.4800.0000.1570.1220.0000.1400.0000.0000.0000.3160.2670.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0770.0000.0000.1240.0810.0000.0000.0000.0000.0000.250
gaestewc0.0000.1580.3180.2860.2550.1400.0000.2170.0000.0000.0000.0000.0000.0000.0490.0000.0000.0000.0000.0000.0000.0650.3130.1090.0000.1860.0000.0970.2890.0000.0000.0000.1050.0000.1230.1151.0000.1240.2220.0000.0000.1800.0000.1360.0000.0600.1500.0000.0000.0000.3010.0970.0790.0590.0000.0000.1300.0000.0000.2750.0000.0000.0000.0000.0000.2590.0640.0000.2370.2260.0000.0000.0000.0690.0000.0000.0000.0550.0000.0140.0000.1260.0300.0000.0000.1230.0000.3840.0000.0000.0000.000
garage0.1940.0000.1890.0700.2420.0000.0000.1000.0000.0000.0000.0000.1720.1920.0000.0000.0000.1070.3080.0000.0000.2420.0000.1230.0000.1290.0000.0000.0000.0000.2200.0000.0000.0000.2240.1340.1241.0000.1500.0540.0860.1520.0000.0000.1060.1740.0000.0000.0700.0000.2250.0000.2840.0000.0000.1420.1630.0690.0000.2330.0000.0650.2320.0000.0530.0000.0000.0000.1720.2250.0000.0000.0000.1990.0000.2050.1880.1990.0000.0000.1150.0910.2520.0000.1020.0000.0000.0000.1480.0000.0610.186
garten0.0000.0000.1710.1310.0000.0000.0000.3580.0000.0000.0000.0000.0000.0750.0000.0000.1610.0000.1590.0000.0000.0930.2230.3930.0000.2030.0000.0000.2970.0000.1020.0000.3090.0000.1650.0100.2220.1501.0000.0000.1450.0000.0000.0200.1170.0000.1040.0000.0580.0000.1750.0000.2690.0720.1590.0400.1000.1220.0000.2270.0000.0000.0000.0630.0890.1760.0400.0000.0650.0370.0000.1800.0000.1020.0000.1180.0000.1540.0610.0490.0490.3040.0630.0000.3510.0000.1180.2900.1790.0000.0000.000
gartennutzung0.0000.0000.0000.0000.0000.0000.0000.1430.0000.0000.0000.0000.0000.0000.0000.2300.0000.0000.0490.0000.0000.0880.0360.0000.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.1950.0000.0000.0540.0001.0000.0000.0000.0000.0000.1220.0000.0000.0000.0000.0000.0000.1220.1660.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3560.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
gas0.1690.1210.0000.0000.2860.2650.0000.1370.0000.0000.0000.0000.0000.1450.0000.0000.1250.0000.0790.0000.0550.1910.0630.0000.1070.1720.0000.1150.0000.0000.0380.0000.1150.0000.1790.0940.0000.0860.1450.0001.0000.1250.0000.0000.0550.0380.1380.0000.0000.0370.2820.0550.0770.1370.0790.0000.2100.0000.0000.2550.0550.0000.4580.0370.0000.0000.0000.0000.2210.2010.0000.0000.0000.1940.0000.0000.0000.1380.0000.0000.0000.2040.2960.0000.2100.1540.0000.0580.1270.0000.0000.265
gepflegt0.0000.0000.0740.0000.1750.2460.0000.0000.0000.0000.0000.0000.0500.0580.0000.0000.0000.0000.1080.0000.0000.0000.0660.1820.0000.0700.0000.0000.0660.0000.0000.0000.0000.0000.1130.1190.1800.1520.0000.0000.1251.0000.0000.0000.0000.0000.1560.0000.0380.0000.1550.0000.0660.0000.0700.0800.1020.0710.0000.1610.0000.0360.0000.0000.0000.0000.0000.0000.1100.1280.0000.1670.0000.0000.0000.0000.0000.0000.0000.0000.0420.0770.1690.0000.1450.0000.0800.0000.0900.0000.0400.207
granit0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1620.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0840.0000.0000.0000.0470.0840.0000.0000.0000.0000.0000.0001.0000.0000.2020.0840.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
haustiere_erlaubt0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0590.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.0000.0000.1360.0000.0200.0000.0000.0000.0001.0000.0000.0000.3540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0960.0000.0000.0000.0000.0910.0000.0000.0000.1290.0880.0000.0000.0000.000
holz0.0000.0000.0000.0000.0000.2240.0000.0000.0000.0000.0000.0000.0000.0000.0000.1220.2160.0000.1640.0000.0000.0000.0000.2420.0000.0000.0000.0000.0880.0000.1080.0000.0500.0000.0590.1080.0000.1060.1170.1220.0550.0000.2020.0001.0000.0000.0000.0000.0000.1280.0000.0000.1530.0000.0000.0000.0000.0000.2020.0000.0000.0000.0000.1510.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1960.0000.0000.0000.0000.0800.0000.0970.0000.0000.0000.0000.0390.1180.0000.0000.000
holzfenster0.0000.1180.0000.0000.0000.2270.0000.0000.0000.0000.0000.0840.3380.0000.0000.0000.0570.1840.2210.0000.0000.1840.1460.0220.0000.0000.0000.0000.1920.0000.2230.0000.0880.0000.4800.0000.0600.1740.0000.0000.0380.0000.0840.0000.0001.0000.0000.0000.0130.1710.0000.1080.0000.0280.0000.1620.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0770.0000.0000.0000.1240.0000.0280.1080.0000.0000.0000.0000.0000.0000.0000.0650.0000.0000.2210.1570.0000.0000.081
kable_sat_tv0.0000.0700.1130.0000.0000.1100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1050.0000.0000.0000.0000.0000.0350.0440.0000.0000.0000.0000.0000.1500.0000.1040.0000.1380.1560.0000.3540.0000.0001.0000.0000.0000.0140.0820.0420.0480.0000.0000.0000.0000.0000.0000.0880.0000.0000.0000.0000.0000.0000.0000.0000.0330.1280.0000.0000.0000.0000.0000.0000.0000.0500.0000.0000.0000.0000.1130.0000.0000.0000.1850.0000.0330.0000.0000.114
kamera0.0490.3960.0000.0000.1890.0000.0000.0000.4930.0000.0000.0000.0000.0000.1060.0000.1620.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0840.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0530.0000.0000.0000.0000.0000.0000.0000.0980.0000.0000.0000.0000.0900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2730.0000.0000.0000.0000.0000.1790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0900.0000.0000.000
kamin0.1070.2710.0000.0000.0000.0430.0000.0000.0530.0000.0000.0000.0000.1450.0000.0000.0000.0000.0000.0000.0000.0000.0000.2090.0000.0000.0000.0000.0790.0000.0000.0000.0000.0000.0000.0000.0000.0700.0580.0000.0000.0380.0000.0000.0000.0130.0000.0531.0000.0000.0650.0000.0000.0000.0000.0000.0000.0000.0000.0710.1010.0000.0240.0000.0000.0000.0000.0000.0000.0000.0300.0770.0000.0000.1330.0000.0670.2370.1640.0000.0000.0000.0580.0000.1120.0000.0000.1020.0000.0000.0000.065
kapitalanlage0.2130.3180.4260.5400.0000.0000.0000.0000.0000.0000.0000.0980.0340.0000.0000.0000.0000.0000.1480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0370.0000.0000.0000.1280.1710.0140.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0130.0980.0000.0000.0000.0000.1320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0910.0000.0000.032
kfw550.2360.1110.0380.0000.3380.1480.0000.1190.0000.0000.0000.0000.0000.2640.0300.0000.0000.0000.0000.0000.0000.0000.1290.1730.0000.6440.0000.0000.0000.0000.0000.0000.1350.0000.0710.0000.3010.2250.1750.0000.2820.1550.0000.0000.0000.0000.0820.0000.0650.0001.0000.0000.0000.0930.0000.0000.4070.1090.0000.4990.0000.1780.1580.0000.0000.1110.0000.0000.4520.5370.0000.0770.0000.0000.0000.0000.0000.2040.0000.0000.0000.1110.3640.0000.3590.1150.0000.0460.0000.0000.0000.175
kunststoff0.0000.0000.0000.0000.0000.0000.0000.1250.0000.0000.0000.0000.0000.0000.0000.1220.0000.0000.0000.0000.0000.2160.0880.0600.0000.0000.0000.0000.0880.0000.0000.0000.0000.0000.2980.0000.0970.0000.0000.1220.0550.0000.0000.0000.0000.1080.0420.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1960.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1340.0000.0000.0000.000
kunststofffenster0.1560.1030.0930.1190.1220.1360.0000.1940.0000.0000.0000.0000.2190.0000.0000.0000.0000.2750.2690.0000.1530.1880.2120.2420.0000.0000.0000.0000.2470.0000.1740.0000.1480.0000.3080.0000.0790.2840.2690.1660.0770.0660.0000.0000.1530.0000.0480.0000.0000.0000.0000.0001.0000.2090.0000.1620.0000.2340.0000.0000.0000.0000.0000.0000.1920.0000.0000.0000.0000.0090.0000.0000.0000.2690.0000.2450.0000.1400.0000.0000.1070.0610.0790.0000.0310.0480.0000.2030.3140.0000.1220.115
laminat0.0000.0000.0820.1250.1810.0000.0000.1100.0000.0000.0000.0190.0000.3050.0160.0000.0000.0000.2780.0000.0190.1010.2290.1350.0000.0000.0000.0000.2650.0000.1820.0000.3180.0000.1030.0570.0590.0000.0720.0000.1370.0000.0000.0000.0000.0280.0000.0000.0000.0000.0930.0000.2091.0000.0130.0000.0350.2470.0000.0980.0000.0000.0000.0000.0000.1780.0000.0000.0470.1390.0000.0000.0000.1260.0000.0000.0000.0000.0000.0000.0000.0740.0000.0000.0660.1200.0760.1520.1320.0000.1280.140
linoleum0.0000.0000.0000.0000.0000.0000.0000.0840.0000.0000.0000.1240.0000.0000.0000.0000.0000.0000.0000.0490.1640.0000.1190.0000.0000.0000.0000.0000.1190.0000.0000.0000.3730.0000.0000.0000.0000.0000.1590.0000.0790.0700.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0131.0000.0000.0000.1020.0000.0000.0000.0000.0000.0480.0000.0000.0000.0000.0000.0000.0000.3070.0000.1910.0000.0000.0000.0000.0000.0000.0000.2450.0000.0000.1340.0000.2300.1810.1360.0000.0000.029
loggia0.0000.0000.0990.0000.0440.1450.0000.1300.0000.0000.0000.0000.1130.0000.0000.0000.0000.1470.2300.0000.0000.1470.1830.2690.0000.0000.0000.0000.1830.0000.0280.0000.0000.0000.0000.0000.0000.1420.0400.0000.0000.0800.0000.0000.0000.1620.0000.0000.0000.0000.0000.0000.1620.0000.0001.0000.0000.2230.0000.0000.0000.0000.0750.0840.0000.0000.0000.0000.0000.0000.0000.0000.0000.2300.0000.0000.0000.0000.0000.0000.0150.1430.0000.0000.1580.0000.0000.1390.1730.0000.0000.000
luftwp0.0000.0000.0000.0000.6120.1880.0000.0000.0980.0000.0000.0000.0000.1460.0000.0000.0000.0000.0000.0000.0000.0000.1460.0490.1020.4420.0000.0000.0000.0000.0000.1280.0000.0000.0000.4140.1300.1630.1000.0000.2100.1020.0000.0000.0000.0000.0000.0980.0000.0000.4070.0000.0000.0350.0000.0001.0000.0180.0000.5290.0000.1840.1050.0000.0000.0000.0000.0000.0000.3060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0590.0000.0000.2260.0630.0000.1390.0000.0000.0000.000
massivhaus0.0440.0470.0960.1280.0380.1240.0000.0000.0000.0000.0000.0000.3220.0000.0000.0000.0160.1050.2310.0000.0780.2560.1230.0570.1370.0740.0000.0000.2150.0000.0000.0000.1180.0000.0000.0580.0000.0690.1220.0000.0000.0710.0000.0000.0000.0000.0000.0000.0000.0130.1090.0000.2340.2470.1020.2230.0181.0000.0000.0630.0000.0430.0590.0000.0000.0000.0000.0000.0430.1740.0000.0620.0000.2310.0520.0000.0000.0890.0330.0000.0000.1960.1200.0000.1270.0510.3570.0270.0830.1080.0330.187
moebliert0.0000.0000.0000.0000.1890.2790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0840.0000.0000.0000.0000.0000.0000.0000.0000.2020.0000.0000.0000.0000.0980.0000.0000.0000.0000.0000.0000.0000.0001.0000.0470.0000.0000.0000.1140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
neubau0.0000.0000.0000.0000.5980.0000.0000.1250.0000.0000.0000.0000.0000.2730.0000.0000.0000.0000.0000.0000.0000.0000.1510.1350.1560.6260.0000.0000.0000.0000.0000.0590.0330.0000.0000.4800.2750.2330.2270.0000.2550.1610.0000.0000.0000.0000.0880.0000.0710.0000.4990.0000.0000.0980.0000.0000.5290.0630.0471.0000.0000.0000.1650.0000.0000.0520.0000.0000.2370.6610.0000.0830.0000.0000.0000.0000.0000.1710.0000.0000.0000.1170.2300.0000.3350.1210.0000.1590.0000.0000.0000.155
neuwertig0.2070.3920.0840.2330.3580.3410.0000.0000.0000.0000.0000.0000.0000.0000.0000.1460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1030.0000.0000.0000.0000.0000.0000.0550.0000.0000.0000.0000.0000.0000.0000.1010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0740.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.047
nicht_unterkellert0.1400.0000.0000.0000.3150.0000.0000.0000.0900.0000.0000.0000.0000.1100.0000.0000.0000.0000.0000.0000.0000.0000.1430.0640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1570.0000.0650.0000.0000.0000.0360.0000.0440.0000.0000.0000.0900.0000.0000.1780.0000.0000.0000.0000.0000.1840.0430.0000.0000.0001.0000.0430.0000.0000.0000.0000.0000.1700.1000.0000.0230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2850.0000.0000.1430.0000.0000.0000.140
oel0.0590.0000.0000.0000.3220.3320.0000.0000.0000.0000.0000.0000.0420.0810.0000.0000.0160.0000.0000.0000.0000.0160.0730.1370.0000.0740.0000.0000.0000.0000.0000.0000.0000.0000.0000.1220.0000.2320.0000.0000.4580.0000.0000.0000.0000.0000.0000.0000.0240.0000.1580.0000.0000.0000.0000.0750.1050.0590.0000.1650.0000.0431.0000.0000.0000.0000.0000.0000.1140.1740.0000.0000.0000.0740.0000.0000.0000.0440.0000.0360.0000.0000.0000.0000.1270.0000.0000.0000.0000.0000.0000.187
ofenheizung0.0000.0000.2360.1310.0000.0000.0000.0000.0000.0000.1250.0000.0000.0520.0000.0000.0000.0000.1750.0000.0000.0000.0000.0520.1250.0000.0000.0660.0000.0000.1080.0000.0000.0000.0000.0000.0000.0000.0630.0000.0370.0000.0000.0000.1510.0000.0000.0000.0000.1320.0000.0000.0000.0000.0480.0840.0000.0000.1140.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0480.0000.0840.0000.0570.0000.0000.0000.0000.0000.0000.0000.0000.0840.0000.0000.0000.0000.055
offene_k\u00fcche0.0000.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1390.0000.0130.0000.0000.0000.1390.0000.0000.0470.0000.0000.2270.1400.0000.0530.0890.1460.0000.0000.2310.0000.0470.0000.0000.0000.0000.0000.0000.0000.1920.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
parkett0.0000.0000.1280.1610.0000.1590.0000.1610.0000.0000.0000.0000.0000.2990.0000.0000.0000.0000.0000.0000.0000.0000.0390.0800.0000.0000.0000.0000.0890.0000.0000.0000.3670.0000.0000.0000.2590.0000.1760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1110.0000.0000.1780.0000.0000.0000.0000.0000.0520.0000.0000.0000.0000.0001.0000.0000.0000.0680.1110.0000.0000.0000.0000.0000.0000.2360.0000.0000.2260.0000.3110.0000.0000.0940.0000.0440.1820.0000.0000.0000.000
pellet0.1610.0000.0000.0000.2890.1140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0640.0000.0400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.000
personenaufzug0.0490.6780.3230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3410.0000.0000.0000.0000.0000.0000.0000.000
projektiert0.2010.0000.0000.0000.1890.1070.0000.0770.0000.0000.0000.0000.0000.2060.1050.0000.0000.0000.0000.0000.0000.0000.1430.1270.0000.0000.0000.0000.1900.0000.0000.0000.1330.0000.0000.3160.2370.1720.0650.0000.2210.1100.0000.0000.0000.0000.0330.0000.0000.0000.4520.0000.0000.0470.0000.0000.0000.0430.0000.2370.0000.1700.1140.0000.0000.0680.0000.0001.0000.4020.0000.0230.0000.0000.0000.0000.0000.1540.0000.0000.0000.0680.2890.0000.2850.0730.0000.1890.0000.0000.0000.321
provisionsfrei0.2160.1370.0000.0000.1830.0000.0000.1230.0000.0000.0000.0000.0000.2420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.5160.0000.0000.0000.0000.0000.0000.1240.0000.1170.2670.2260.2250.0370.0000.2010.1280.0000.0000.0000.0770.1280.0000.0000.0000.5370.0000.0090.1390.0000.0000.3060.1740.0000.6610.0000.1000.1740.0000.0000.1110.0000.0000.4021.0000.0000.0620.0000.0000.0470.0000.0000.1230.0000.0000.0000.0540.2410.0000.2680.1170.0000.1120.0680.0000.0000.155
renoviert0.1090.3580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1200.0000.0000.0000.0000.0000.0000.0000.0000.0170.0600.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0600.0000.0000.0000.0000.0850.0000.0000.0000.0000.0000.0000.1220.0000.0000.0000.0000.0000.0000.000
renovierungsbed\u00fcrftig0.1080.0000.0840.0000.1860.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0890.0410.0000.0000.2070.0000.0000.0000.0000.0000.1800.0000.0000.1670.0000.0000.0000.0000.0000.0000.0770.0000.0770.0000.0000.0000.3070.0000.0000.0620.0000.0830.0000.0230.0000.0280.0000.0000.0000.0000.0230.0620.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0450.1380.0000.1430.0000.0990.0970.0000.0000.0000.000
rollstuhlgerecht0.1740.1470.0000.0000.0000.0000.0000.0000.2730.0000.0600.0000.0000.0000.2130.0000.0410.0000.0000.0000.0000.0000.0000.0000.0600.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2730.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0600.0001.0000.0000.0000.0000.0000.0000.0600.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0600.000
sat0.0000.2870.2580.2990.0000.1700.0000.0840.0000.0000.0000.1240.0810.0000.0000.0000.0000.1170.4110.0490.1640.5110.1760.0800.0000.0000.1240.0810.0490.0000.2210.0000.0000.0000.2400.0000.0690.1990.1020.0000.1940.0000.0000.0000.0000.1240.0000.0000.0000.0000.0000.0000.2690.1260.1910.2300.0000.2310.0000.0000.0000.0000.0740.0480.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0270.0000.0000.0160.0000.0000.0000.1740.0000.0000.1890.0000.0000.1810.3290.0000.0000.000
sauna0.2140.3910.0000.0000.2850.0000.0000.0000.0000.0180.0000.1060.0510.0170.1310.0180.0000.0000.0270.0000.0000.0910.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1330.0000.0000.0000.0000.0000.0000.0000.0000.0520.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0000.0000.0271.0000.0000.0000.0000.2490.0000.0000.0000.0000.0000.0540.0000.0000.0000.0000.0000.0000.000
speisekammer0.0000.0000.0000.0000.0440.0000.0000.3580.0000.0000.0000.0000.0000.0000.0000.0740.0000.0000.2300.0000.0000.0000.0400.2040.0000.0000.0000.0000.1200.0000.1620.0000.0390.0000.3760.0000.0000.2050.1180.3560.0000.0000.0000.0000.1960.0280.0000.0000.0000.0000.0000.1960.2450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0840.2300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0960.0000.0000.0150.0440.0910.0000.0000.0000.0000.1390.2800.0000.0000.135
stein0.0500.1100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0590.0000.0000.1880.0000.0000.0000.0000.0000.0000.0000.1080.0000.0000.0670.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0090.0000.0000.0000.1360.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
stellplatz0.1610.1820.0450.0490.0000.0000.0000.0000.0000.0000.0500.0000.0000.0600.0740.0710.0000.0410.0000.0000.0000.0000.0000.1450.0500.1130.0000.0000.0000.0000.1050.0000.0000.0000.2990.0000.0550.1990.1540.0000.1380.0000.0000.0960.0000.0000.0500.0000.2370.0000.2040.0000.1400.0000.0000.0000.0000.0890.0000.1710.0740.0000.0440.0570.0000.0000.0000.0000.1540.1230.0850.0000.0000.0160.0000.0960.0091.0000.0000.0000.0000.0190.2150.0000.0430.0000.0870.0000.0680.0000.0000.151
swimmingpool0.0000.4090.0730.0000.3310.0000.0000.0000.1790.0000.0000.0000.0000.0000.2490.0000.0000.0000.0000.0000.0000.0000.0000.1200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0610.0000.0000.0000.0000.0000.0000.0000.0000.1790.1640.0000.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0600.0000.2490.0000.0000.0001.0000.0000.0600.0000.1190.0000.0000.0000.0000.1170.0000.0000.0000.000
teil_saniert0.0000.0000.0000.0000.0000.0000.0000.0830.0000.0000.0000.0000.0000.0880.0000.0000.0000.0000.0000.0000.0000.0000.1000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1510.0000.0000.0000.0140.0000.0490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0360.0000.0000.2260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0640.0000.0000.0000.0000.0670.0000.0000.0600.083
teilweise_m\u00f6bliert0.1650.1470.0000.2590.0000.0000.0000.0830.0000.0000.0000.0000.0000.0880.0000.0000.0410.0000.2410.0000.0000.0410.0000.1580.0000.0000.0000.0000.0000.0000.1810.0800.0000.0000.0000.0000.0000.1150.0490.0000.0000.0420.0000.0000.0800.0000.0000.0000.0000.0000.0000.0000.1070.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0600.0001.0000.0930.0140.0000.0210.0000.0000.0000.1910.0000.0600.000
teppich0.2130.0000.1280.1030.1780.2020.0000.3310.0000.0000.0000.0000.0000.1320.0000.0000.0000.0000.2450.0000.0000.2520.2640.1640.0000.0000.0000.0000.2640.0000.0000.0000.4720.0000.0000.0770.1260.0910.3040.0000.2040.0770.0000.0000.0000.0000.0000.0000.0000.0000.1110.0000.0610.0740.2450.1430.0590.1960.0000.1170.0000.0000.0000.0000.0000.3110.0000.0000.0680.0540.0000.0450.0000.1740.0000.0440.1360.0190.0000.0000.0931.0000.1260.0000.2640.0000.0440.3110.1650.0000.0000.152
terrasse0.0870.0000.1430.1350.0000.0000.0000.0800.0000.0000.0000.0000.0000.1170.0000.0000.0000.0000.0690.0000.0000.0650.0000.1400.0000.2300.0000.0000.0430.0000.0000.0000.0170.0000.1230.0000.0300.2520.0630.0000.2960.1690.0000.0910.0970.0000.1130.0000.0580.0000.3640.0000.0790.0000.0000.0000.0000.1200.0000.2300.0000.0000.0000.0000.0000.0000.0140.0000.2890.2410.0000.1380.0140.0000.0000.0910.0000.2150.1190.0640.0140.1261.0000.0000.2390.0000.0000.0560.1460.0000.0200.388
tiefgarage0.0000.4550.1760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0610.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
unterkellert0.0920.1830.0000.0000.2880.0000.0000.1910.0000.0000.0000.0000.0000.3250.0000.0000.0680.0000.1340.0000.0000.0680.1210.3670.0770.2270.0000.0000.1990.0000.1190.0000.1430.0000.0850.1240.0000.1020.3510.0000.2100.1450.0000.0000.0000.0650.0000.0000.1120.0000.3590.0000.0310.0660.1340.1580.2260.1270.0000.3350.0000.2850.1270.0000.0000.0940.0000.0000.2850.2680.1220.1430.0000.1890.0540.0000.0000.0430.0000.0000.0210.2640.2390.0001.0000.0000.0000.3000.1000.0000.0000.186
vermietet0.0000.0000.2570.2460.0000.0000.0000.0470.0000.0000.0000.0000.0000.0750.0000.0000.0000.0680.0000.0000.0000.0680.0710.0000.0000.0200.0000.0000.0000.0000.0750.0000.0000.0000.0000.0810.1230.0000.0000.0000.1540.0000.0000.0000.0000.0000.0000.0000.0000.0000.1150.0000.0480.1200.0000.0000.0630.0510.0000.1210.0000.0000.0000.0000.0000.0000.0000.0000.0730.1170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.092
vollerschlossen0.1460.0000.0000.0000.3000.0000.0000.0000.0000.0000.1690.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1200.0000.1690.0000.0000.0000.1200.0000.0000.0000.1260.0000.0000.0000.0000.0000.1180.0000.0000.0800.0000.1290.0000.0000.1850.0000.0000.0000.0000.0000.0000.0760.2300.0000.0000.3570.0000.0000.0000.0000.0000.0840.0000.0440.0000.0000.0000.0000.0000.0990.0000.0000.0000.0000.0000.0870.0000.0000.0000.0440.0000.0000.0000.0001.0000.0700.0000.0000.0000.000
wanne0.2270.1820.2220.1020.1060.1100.0000.3360.0000.0000.0000.0000.0000.2050.0000.0000.0000.0190.1260.0000.0000.1140.6940.2820.0000.2340.0000.0300.6940.0000.0820.0390.3600.0000.1980.0000.3840.0000.2900.0000.0580.0000.0000.0880.0390.2210.0000.0000.1020.0000.0460.1340.2030.1520.1810.1390.1390.0270.0000.1590.0000.1430.0000.0000.0000.1820.0000.0000.1890.1120.0000.0970.0000.1810.0000.1390.0000.0000.1170.0670.0000.3110.0560.0000.3000.0000.0701.0000.1590.0000.0000.156
wasch_trockenraum0.0000.0000.0000.0640.0000.0650.0000.2680.0900.0000.0000.0900.0000.0000.0000.0000.0680.0000.4210.0000.0000.3080.0640.1740.0000.0000.0000.0000.0640.0000.2380.1180.2140.0000.1680.0000.0000.1480.1790.0000.1270.0900.0000.0000.1180.1570.0330.0900.0000.0910.0000.0000.3140.1320.1360.1730.0000.0830.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0680.0000.0000.0000.3290.0000.2800.0000.0680.0000.0000.1910.1650.1460.0000.1000.0000.0000.1591.0000.0000.0000.117
wg_geeignet0.2240.0490.0000.0000.1210.0000.0000.0000.0000.0000.0000.0000.0610.0000.0000.0000.0000.0000.0000.0000.1220.0000.0000.0000.0000.0000.0000.0610.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
wintergarten0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0610.0000.0000.0000.0400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1220.1280.0000.0000.0000.0330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0600.0000.0000.0000.0000.0000.0000.0600.0600.0000.0200.0000.0000.0000.0000.0000.0000.0001.0000.000
zentralheizung0.0000.0000.0000.0000.2110.0950.0000.1050.0000.0000.0000.0000.0000.2170.0000.0000.0000.0000.0290.0000.0000.0320.0460.2550.0000.0000.0000.2390.0460.0000.0810.0000.0670.0000.1670.2500.0000.1860.0000.0000.2650.2070.0000.0000.0000.0810.1140.0000.0650.0320.1750.0000.1150.1400.0290.0000.0000.1870.0000.1550.0470.1400.1870.0550.0000.0000.0000.0000.3210.1550.0000.0000.0000.0000.0000.1350.0000.1510.0000.0830.0000.1520.3880.0000.1860.0920.0000.1560.1170.0000.0001.000

Missing values

2023-07-12T17:01:15.328537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-12T17:01:16.583651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0Object_priceLivingSpaceRoomsConstructionYearZipCodeEstateTypeDistributionTypeabrissobjektabstellraumalarmanlageals_ferienimmobilie_geeignetaltbau_(bis_1945)aluminiumfensterbad/wc_getrenntbalkonbarriefreibidetcarportdach_ausbauf\u00e4higdach_ausgebautdenkmalgeschuetztdielendslduscheeinbauk\u00fccheelektroerstbezugestrichetagenheizungfensterfernfernefertighausfliesenfluessiggasfreifu\u00dfbodenheizunggaestewcgaragegartengartennutzunggasgepflegtgranithaustiere_erlaubtholzholzfensterkable_sat_tvkamerakaminkapitalanlagekfw55kunststoffkunststofffensterlaminatlinoleumloggialuftwpmassivhausmoebliertneubauneuwertignicht_unterkellertoelofenheizungoffene_k\u00fccheparkettpelletpersonenaufzugprojektiertprovisionsfreirenoviertrenovierungsbed\u00fcrftigrollstuhlgerechtsatsaunaspeisekammersteinstellplatzswimmingpoolteil_saniertteilweise_m\u00f6bliertteppichterrassetiefgarageunterkellertvermietetvollerschlossenwannewasch_trockenraumwg_geeignetwintergartenzentralheizung
00299000186.65.01927.097261HOUSEBUY00000000000000000000000000000000001000000000000000000000000000000100000000001010000000
111295000190.07.01978.097074HOUSEBUY00000011000000001000001000001011000000010000000000000000000100000000001000001010010000
22595000212.014.01988.097222HOUSEBUY01000001101010001100001011100001101100100001001000000000010000000000010000111010011001
33499000137.04.01984.097084HOUSEBUY00000000000000000000000000000000000000000000000000000000000000000000000000000000000000
44329000128.07.01920.097222HOUSEBUY00000000000000000000000000000000101100000000000000000000000000000000000000001010001001
55629000225.07.02012.097234HOUSEBUY00000000000000000100000000000110101000100001000000001100010000010000000000001010010000
66700000170.07.01984.097076HOUSEBUY00001000100000000100000000000010101000000001000000000000010000011010000000001010010001
77690000210.07.0NaN97082HOUSEBUY00000011000000000000001000000010101000000000001100010001000100000000000000010001000101
88735000128.66.01990.097076HOUSEBUY00000001000000000000000000000000001101001000000000000000000000000000000100001000000001
99409000143.06.01995.097084HOUSEBUY01000000001000001100000000000010101100000000001000000000000100000000000000001011010001
Unnamed: 0Object_priceLivingSpaceRoomsConstructionYearZipCodeEstateTypeDistributionTypeabrissobjektabstellraumalarmanlageals_ferienimmobilie_geeignetaltbau_(bis_1945)aluminiumfensterbad/wc_getrenntbalkonbarriefreibidetcarportdach_ausbauf\u00e4higdach_ausgebautdenkmalgeschuetztdielendslduscheeinbauk\u00fccheelektroerstbezugestrichetagenheizungfensterfernfernefertighausfliesenfluessiggasfreifu\u00dfbodenheizunggaestewcgaragegartengartennutzunggasgepflegtgranithaustiere_erlaubtholzholzfensterkable_sat_tvkamerakaminkapitalanlagekfw55kunststoffkunststofffensterlaminatlinoleumloggialuftwpmassivhausmoebliertneubauneuwertignicht_unterkellertoelofenheizungoffene_k\u00fccheparkettpelletpersonenaufzugprojektiertprovisionsfreirenoviertrenovierungsbed\u00fcrftigrollstuhlgerechtsatsaunaspeisekammersteinstellplatzswimmingpoolteil_saniertteilweise_m\u00f6bliertteppichterrassetiefgarageunterkellertvermietetvollerschlossenwannewasch_trockenraumwg_geeignetwintergartenzentralheizung
167167499000143.005.01983.097078HOUSEBUY01000000000000001000001000100010101000000000000010000000000000000000000000011010010001
168168550000231.007.01977.097084HOUSEBUY00000001000100000100001010101010001000010000000100000000000100000000010100001010010001
169169375000106.605.01974.097076HOUSEBUY01000001000000001000001000100010100101001000000100010000100100000000000000001010110001
170170572900162.005.0NaN97080HOUSEBUY00000000000000001001001000000010000000000000100000000100000000010000000000000000010000
171171429000109.374.51969.097078HOUSEBUY00000000000000000000000100000001000000001000000000000000000000000100000000001010000000
172172698700144.814.0NaN97080HOUSEBUY00000000000000001001001000000010000000000000100000000100000000010000000000000000010000
173173699800144.814.0NaN97080HOUSEBUY00000000000000001001001000000010000000000000100000000100000000010000000000000000010000
174174595000200.005.01980.097084HOUSEBUY00000000000000001110001000000011100000000000000001010000010000000001000000000010010000
175175180000318.9521.01966.097080HOUSEBUY10000000000000000000000000000000000000000000000000000000000000000000000000000000000000
176176785000190.304.51953.097204HOUSEBUY01000010000010011100001010001011101000010000000000010000000000000001101000011010010001